Adaptive multisensor target detection using feature-based fusion

Kwon, Heesung; Der, Sandor Z.; Nasrabadi, Nasser M.

doi:10.1117/1.1425788

Cited by 21 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A detection algorithm described elsewhere [9,10] was applied to the test imagery. The recognition algorithm takes as input the original image and the detection file produced by the detector.…”

Section: Resultsmentioning

confidence: 99%

Automatic Target Recognizer for the Demo III Program

Der¹

2002

View full text Add to dashboard Cite

This report describes an algorithm for the recognition of military vehicles in Forward Looking Infrared (FLIR) imagery. The input is a FUR image, and the output of a detector or clutter rejector listing a number of locations in the image for the recognizer to examine. The output is the same list with the decision of the recognizer appended to each location in the list. The algorithm is based on principal component analysis.

show abstract

Section: Resultsmentioning

confidence: 99%

Automatic Target Recognizer for the Demo III Program

Der¹

2002

View full text Add to dashboard Cite

show abstract

“…44 (6) range and azimuth errors of 0.8 cm and 1.1 deg, respectively. Referring to Tables 1 to 3, note that the range estimation accuracy improves with increasing correct classification rate, whereas the azimuth estimation accuracy is independent of it, as expected, because of the way it is estimated.…”

Section: -6 Optical Engineeringmentioning

confidence: 96%

“…44 (6) angular values where the maximum intensity values occur is calculated to find the azimuth estimate of the surface. Then, the observed scan is shifted by the azimuth estimate and the model represented by Eq.…”

Section: -4 Optical Engineeringmentioning

confidence: 99%

“…44 (6) For a set of six surfaces including Styrofoam packaging material; white painted matte wall; white or black cloth; and white, brown, and violet paper ͑also matte͒, we get a correct differentiation rate of 100% and the surfaces are located with absolute range and azimuth errors of 0.2 cm and 1.1 deg, respectively. We can increase the number of surfaces differentiated at the expense of a decrease in the correct differentiation rate.…”

Section: -5 Optical Engineeringmentioning

confidence: 99%

“…Examples of work in this category include face identification, 4 automatic vehicle detection, 5 automatic target recognition 6 and tracking, 7 detection and identification of targets in background clutter, 8 remote sensing, and automated terrain analysis. 9 IR sensors are used in robotics and automation, process control, remote sensing, and safety and security systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Surface differentiation by parametric modeling of infrared intensity scans

Aytaç

Barshan

2005

Opt. Eng

View full text Add to dashboard Cite

Abstract. We differentiate surfaces with different properties with simple low-cost IR emitters and detectors in a location-invariant manner. The intensity readings obtained with such sensors are highly dependent on the location and properties of the surface, which complicates the differentiation and localization process. Our approach, which models IR intensity scans parametrically, can distinguish different surfaces independent of their positions. Once the surface type is identified, its position (r,) can also be estimated. The method is verified experimentally with wood; Styrofoam packaging material; white painted matte wall; white and black cloth; and white, brown, and violet paper. A correct differentiation rate of 100% is achieved for six surfaces, and the surfaces are localized within absolute range and azimuth errors of 0.2 cm and 1.1 deg, respectively. The differentiation rate decreases to 86% for seven surfaces and to 73% for eight surfaces. The method demonstrated shows that simple IR sensors, when coupled with appropriate signal processing, can be used to recognize different types of surfaces in a location-invariant manner.

show abstract

Evolutionary Sensor Fusion for Security

Bhanu

Nadimi

2006

Advanced Sciences and Technologies for Security Applications

View full text Add to dashboard Cite

Adaptive multisensor target detection using feature-based fusion

Cited by 21 publications

References 8 publications

Automatic Target Recognizer for the Demo III Program

Automatic Target Recognizer for the Demo III Program

Surface differentiation by parametric modeling of infrared intensity scans

Evolutionary Sensor Fusion for Security

Contact Info

Product

Resources

About